{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use of normalisation function\n",
    "\n",
    "In this example we normalise a spectrum to different values: area, maximum, or min-max."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import numpy as np\n",
    "import scipy\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import rampy as rp\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Signal creation\n",
    "\n",
    "Below we create a fake Gaussian signal for the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_points  =100\n",
    "x = np.sort(np.random.uniform(0,100,nb_points)) # increasing point\n",
    "y = 120.0*scipy.stats.norm.pdf(x,loc=50,scale=5)\n",
    "\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can consider that the area of the Gaussian peak should be equal to 1, as it is the value of the intergral of a Gaussian distribution.\n",
    "\n",
    "To normalise the spectra, we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_norm_area = rp.normalise(y,x=x,method=\"area\")\n",
    "\n",
    "plt.plot(x,y_norm_area)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also just want the signal to be comprised between 0 and 1, so we normalise to the maximum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KUnJ6TyWzN4zli1hllOa6yuylLZGwKBSkpLg7vcMTeTWekNHRVKN7FSR0CgUpKUPjUySn5/KupwCpcYVe3dUsIVMoSEnpG86fhfAW62iq4eTZKZLTs2GXIiVMoSAlJfOXeCKenz0FeCW4RMKgUJCS0jecJBoxWvJoOmpGRzYUdAlJwqNQkJLSOzxBa0M10bL8+9Zvj2empaqnIOHJv58MkQD1DSezl2nyzbqqcppiFeopSKgUClIyMtNRO/JwkDmjvamG3lPqKUh4FApSMkaTM5ydnM3bngKkxhXUU5AwKRSkZGSWkOiI53NPIcbRsUkmZ+bCLkVKlEJBSkZmOmpe9xTSgdU/oktIEo5AQ8HMbjOzQ2bWZWb3n6fdO83MzWx7kPVIaesdThIxaGvI754CoDubJTSBhYKZlQGfBm4HtgH3mNm2JdrVAb8GPBVULSKQmv/f0lBNRTR/O8iZQXDdwCZhCfKnYwfQ5e497j4NfBW4c4l2/wX4E2AywFpE6B3Ov9VRF6uvqaC+plxLaEtoggyFFmBgwfFg+rEsM7seaHP3fwiwDhEg1VPIxzWPFmtviqmnIKEJMhSW2sHEs0+aRYA/Bz664guZ3Wdm+8xs39DQUA5LlFJxOjnN6eRM3vcUILOEtnoKEo4gQ2EQaFtw3AocXXBcB1wDfNfMeoGdwK6lBpvd/UF33+7u25ubmwMsWYrVK6uj5n8otDfFOHr6HFOzmpYqay/IUNgLbDGzhJlVAHcDuzJPuvuYu8fdvcPdO4AngTvcfV+ANUmJyt6jUACXjzqaaph3GBw9F3YpUoICCwV3nwU+DDwKHAQecfcXzOxjZnZHUF9XZCl9w0nMoK0x/0OhXaulSoiiQb64u+8Gdi967IFl2r4lyFqktPUOT7BxXRVV5WVhl7KiTG/msNZAkhDk74RtkRzqPTVREOMJAI2xCuoqo+opSCgUClIS+oaTdOThbmtLMTM64jHtqyChUChI0RtLzjA8MU0ijxfCW6y9qUY9BQmFQkGKXtfQOABXbqgNuZLV62iKMTh6jpm5+bBLkRKjUJCi130yFQpXNBdOKLQ31TA37xzRtFRZYwoFKXpdQ+NURCO05vHqqItlxj90Z7OsNYWCFL3uk+N0xmOURZZaeSU/ZdZo0hLastYUClL0uobGuaKAxhMAmmsrqauM0qNQkDWmUJCiNjkzx8BIkisLaDwBUtNSr9hQy8snxsMuRUqMQkGKWu/wBPNOwfUUIDVbKjNzSmStKBSkqHWlZx4VWk8BYMuGWobOTjGWnAm7FCkhCgUpal0nxzGDzubCuJt5ocx9FV1DZ0OuREqJQkGKWvfQBK0N1QWxEN5imVB4SeMKsoYUClLUXjp+li0b6sIu46K0NdRQX1PO/v7RsEuREqJQkKI1NTtH99A4WzcWZihEIsbrOxp56vBI2KVICVEoSNF6+cQ4s/PO1o3rwi7lot2UaKRvOMnxscmwS5ESoVCQonXw2BmAAg+FJgCeOjwcciVSKhQKUrQOHjtLVXmEjgLZXGcpWzfWUVsZ5WldQpI1olCQonXw2BmuvnxdQa15tFi0LML2jgaNK8iaUShIUXJ3Dh4/w7YCHWReaEeika6T45wanwq7FCkBCgUpSsfGJjmdnCno8YSMzLjCXvUWZA0oFKQoFcMgc8aPtaynqjyiS0iyJhQKUpQyofCaywv/8lFFNMINmzWuIGtDoSBF6eCxs2xurKGuqjzsUnLipkQTLx4/o8XxJHAKBSlKB4+dKdg7mZeyI9GIO+ztVW9BgqVQkKKTnJ7l8PBEUYwnZFy/uZ6KsghPKxQkYAoFKTqHjp/FvTgGmTOqysu4tm29xhUkcAoFKToHj6X2H9hWRKEAqXGF54+MMT41G3YpUsQUClJ0Dh47Q11llNaG6rBLyakdiUbm5p1n+7SUtgRHoSBF5+CxM7xmYx1mhbu8xVJubG+gLGJaHE8CpVCQojI37+mZR8V16QggVhnlmpb1WhxPAhVoKJjZbWZ2yMy6zOz+JZ7/TTM7YGY/NLNvm1l7kPVI8eseGmdieo7XtdaHXUogdiYa+feBMSZn5sIuRYpUYKFgZmXAp4HbgW3APWa2bVGz/cB2d38d8HXgT4KqR0rDD/pPA3BdW3GGwo5EI9Nz8+xPv0+RXAuyp7AD6HL3HnefBr4K3Lmwgbs/5u7J9OGTQGuA9UgJ2D9wmrqqKJ3xwt1D4Xy2dzRipk13JDhBhkILMLDgeDD92HLuBf5pqSfM7D4z22dm+4aGhnJYohSbHwyc5rq2eiIFvIfC+ayvLmfr5es0riCBCTIUlvqp9CUbmr0b2A786VLPu/uD7r7d3bc3NzfnsEQpJsnpWV46cbZoLx1l3NTZyLP9o0zPzoddihShIENhEGhbcNwKHF3cyMzeBvw+cIe7axcRuWjP9I0yN+/c2N4QdimBuinRyOTMPM8d0biC5F6QobAX2GJmCTOrAO4Gdi1sYGbXA/+LVCCcDLAWKQF7uoeJRozXdzSGXUqgMu/vyR5dQpLcCywU3H0W+DDwKHAQeMTdXzCzj5nZHelmfwrUAv/XzH5gZruWeTmRFe3pGeZ1reuJVUbDLiVQTbWVbNlQq3EFCUSgPz3uvhvYveixBxZ8/LYgv76UjvGpWX44OMYH39wZdilrYkeikW/uP8Ls3DzRMt2DKrmj7yYpCnt7R5ibd97QGQ+7lDVxU2cTE9NzHEjvMCeSKwoFKQpPdg9TXmZFP8iccVMiNa7wlMYVJMcUClIUnuge5vq2BqorysIuZU1ctq6KjqYa7a8gOadQkII3dm6GF46OsfOKprBLWVM7Eo3s7R1hfn7J239ELopCQQre04dHmHd4Q2dphcJNiSbGzs1w6MTZsEuRIqJQkIK3p3uYimiE6zcX953Mi+3IjitoHSTJHYWCFLw9PcPcuLmBqvLSGE/IaGusoaW+mqd7Na4guaNQkIJ24swkB4+d4U1bSmMq6mI7Eo08fXgEd40rSG4oFKSgfevACQBu2XZZyJWE46ZEI6fGp+kemgi7FCkSCgUpaN86cIL2phq2bKgNu5RQZMYVtOSF5IpCQQrW+NQse7qHuWXrZZgV5/4JK0nEYzTXVWrTHckZhYIUrG8fPMH03Dy3XnN52KWExszYkWjkqR6NK0huKBSkYO36wVE2rq/ixs2lsbTFcnYmGjl+ZpKBkXNhlyJFQKEgBel0cprvvTzEO67dVLRbb67WjkTqpj1dQpJcUChIQfrGs0eYmXPuvG5T2KWEbsuGWhpqyrUOkuSEQkEKzty88/ATvWxvb+C1m9aHXU7oIund5jQDSXJBoSAF57uHTtI/kuR9N3eEXUre2JFopH8kybExjSvIpVEoSMF56IleLl9Xxa2vLd1ZR4vtTC8G+L2XhkKuRAqdQkEKStfJszz+8inevXMz5dqGMmvbxnVs3biOz3y3m5m5+bDLkQKmnyopKA8/0UdFNMI9OzaHXUpeiUSMj95yFb3DSb7xzGDY5UgBUyhIwTidnOYbzw7yjtdtoqm2Muxy8s5bt27gurZ6Pvntl5mcmQu7HClQCgUpGJ/6ThfnZua47yc6wy4lL5kZv33r1Rwbm+TLT/WHXY4UKIWCFITdzx3j898/zLtubOPqy+vCLidv3XxlnDde0cRffreL5PRs2OVIAVIoSN7b2zvCR772A27Y3MAf3vnasMvJe79169WcGp/mE//yktZDkgumUJC81j00zge+uI+W+mo++57tJbe72sW4YXMDd93Yyuf+7TC//PA+Riamwy5JCohCQfLW0Nkp3vfXT1NmxkPvfz2NsYqwSyoYf/LO1/EH79jG4y+f4u2ffFz7OMuqKRQkLyWnZ7n34b0MnZ3i8+97Pe1NsbBLKihmxvtuTvA3H3oj1RVl3PPZJ/nkv77M3LwuJ8n5KRQk78zOzfPhL+/n+SNjfOqeG7iurT7skgrWNS3r+ftffRN3XtfCn//rS/zS557kxJnJsMuSPKZQkLzi7jyw6wW+8+JJ/vDOa3hbie69nEu1lVE+8a5r+bO7ruXfB8a4/ZOP89iLJ8MuS/KUQkHyhrvzqe908eWn+vngm6/gP+xsD7ukomFmvPPGVv7+V9/EhrpK3v/QXv7oHw8wPaslMeRHBRoKZnabmR0ysy4zu3+J5yvN7Gvp558ys44g65H8tbd3hHf+1R7++7de4o5rN/E7t14ddklF6coNtXzzV27mPW9o57OPH+auv3qC/uFk2GVJHrGg5jGbWRnwEnALMAjsBe5x9wML2nwIeJ27f9DM7gZ+3t1/8Xyvu337dt+3b18gNcvacXe6hyb4fy8N8c39R3juyBgb6ir5yNuu4l3bW4lqsbvA/fPzx/idr/8Qd/jjX/gx3nGtNiwqZmb2jLtvX7FdgKHwBuAP3P3W9PHvArj7f13Q5tF0mz1mFgWOA81+nqIUCvnP3Tk3M8docobRiWlGk9OMTExzOjnDsbFJDh47w4FjZxg6OwXA1o3ruGdHG3fd2EZ1he5DWEsDI0l+/av7ebb/NIl4jCs31HJFcy1XNMfYuL6a9dXl2X91VdGS3/q0kK02FKIB1tACDCw4HgRuWq6Nu8+a2RjQBJzKdTGP7B3gs4/35PplC9pKfw64e6qNp9pmjt3B8dT/DvPuzM07s/POzOw8U7PzTC+zfHN5mXHlhjp+/Mo4N7Q38OarmmlrrMntG5NVa2us4Wv/8Q18cU8fTx8epmdogu8eOsnM3NLfHVXlEaKRCBGDsohRFolQFoEyM8rKjDIzIrYoOGzJD1PHi9sufv4i3lMx+7W3bgm8RxdkKCx1Phd/p62mDWZ2H3AfwObNF7dkcn1NOVsuq72ozy1mttKPnaVOkpml///RYyz9CyFiRMuM8rIIldEy6mvKaagpp76mgsZYBQ015TTUVLC+ulyXhvJMeVmEe9+U4N43JYDUlOCB0XOcPDPJmclZxs7NZP9NzswxO+fZPwTm3JmbS/0/n/7DYOEP8MJO/6t+sFf4q8RX/LOl9KyvLg/8awQZCoNA24LjVuDoMm0G05eP1gOv2mjW3R8EHoTU5aOLKeanX3s5P62dukRWFC2LkIjHSMR1w2ApCvJPtr3AFjNLmFkFcDewa1GbXcB70x+/E/jO+cYTREQkWIH1FNJjBB8GHgXKgC+4+wtm9jFgn7vvAj4PfMnMukj1EO4Oqh4REVlZkJePcPfdwO5Fjz2w4ONJ4K4gaxARkdXTiJ+IiGQpFEREJEuhICIiWQoFERHJUiiIiEhWYGsfBcXMhoC+FZrFCWCpjAJRqu9d77u06H1fuHZ3b16pUcGFwmqY2b7VLPxUjEr1vet9lxa97+Do8pGIiGQpFEREJKtYQ+HBsAsIUam+d73v0qL3HZCiHFMQEZGLU6w9BRERuQhFFwpmdpuZHTKzLjO7P+x6gmJmbWb2mJkdNLMXzOzX0483mtm3zOzl9P8NYdcaBDMrM7P9ZvYP6eOEmT2Vft9fSy/XXlTMrN7Mvm5mL6bP+xtK4Xyb2W+kv8efN7OvmFlVsZ5vM/uCmZ00s+cXPLbkObaUv0j/rvuhmd2QixqKKhTMrAz4NHA7sA24x8y2hVtVYGaBj7r7VmAn8Cvp93o/8G133wJ8O31cjH4dOLjg+L8Bf55+36PAvaFUFaxPAv/s7q8BriX1/ov6fJtZC/BrwHZ3v4bUMvx3U7zn+yHgtkWPLXeObwe2pP/dB3wmFwUUVSgAO4Aud+9x92ngq8CdIdcUCHc/5u7Ppj8+S+oXRAup9/twutnDwM+FU2FwzKwV+Bngc+ljA34K+Hq6SdG9bzNbB/wEqT1IcPdpdz9NCZxvUkv8V6d3Z6wBjlGk59vdv8erd59c7hzfCXzRU54E6s1s46XWUGyh0AIMLDgeTD9W1MysA7geeAq4zN2PQSo4gA3hVRaY/wH8DjCfPm4CTrv7bPq4GM97JzAE/HX6stnnzCxGkZ9vdz8C/BnQTyoMxoBnKP7zvdBy5ziQ33fFFgpL7UJf1NOrzKwW+AbwEXc/E3Y9QTOznwVOuvszCx9eommxnfcocAPwGXe/HpigyC4VLSV9/fxOIAFsAmKkLpssVmznezUC+b4vtlAYBNoWHLcCR0OqJXBmVk4qEP6Pu/+mOaZsAAACUElEQVRN+uETmS5k+v+TYdUXkJuBO8ysl9TlwZ8i1XOoT19egOI874PAoLs/lT7+OqmQKPbz/TbgsLsPufsM8DfAGyn+873Qcuc4kN93xRYKe4Et6ZkJFaQGpHaFXFMg0tfRPw8cdPdPLHhqF/De9MfvBf5urWsLkrv/rru3unsHqfP7HXf/JeAx4J3pZsX4vo8DA2Z2dfqhtwIHKPLzTeqy0U4zq0l/z2fed1Gf70WWO8e7gPekZyHtBMYyl5kuRdHdvGZmbyf1l2MZ8AV3/6OQSwqEmb0JeBx4jleurf8eqXGFR4DNpH6g7nL3xQNXRcHM3gL8lrv/rJl1kuo5NAL7gXe7+1SY9eWamV1HanC9AugB3k/qD7uiPt9m9ofAL5Kacbcf+GVS186L7nyb2VeAt5BaDfUE8J+Bb7LEOU6H5KdIzVZKAu93932XXEOxhYKIiFy8Yrt8JCIil0ChICIiWQoFERHJUiiIiEiWQkFERLIUCiKXIL1a7WEza0wfN6SP28OuTeRiKBRELoG7D5BanfLj6Yc+Djzo7n3hVSVy8XSfgsglSi838gzwBeADwPXpVXpFCk505SYicj7uPmNmvw38M/DTCgQpZLp8JJIbt5Na2vmasAsRuRQKBZFLlF6T6BZSO+D9Ri42OhEJi0JB5BKkFyX7DKn9LPqBPyW1KYxIQVIoiFyaDwD97v6t9PFfAq8xszeHWJPIRdPsIxERyVJPQUREshQKIiKSpVAQEZEshYKIiGQpFEREJEuhICIiWQoFERHJUiiIiEjW/wf08iwupArPJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_norm_area = rp.normalise(y,method=\"intensity\")\n",
    "\n",
    "plt.plot(x,y_norm_area)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if our signal intensity was shifted from 0 by a constant, the \"intensity\" method will not work well. For instance, I can add 0.1 to `y` and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHmZJREFUeJzt3XlwnHed5/H3Vy3Jsm7baslnfEmyHUJOhYSEsXPATAIUoQaGDTXMhFmWbO0MSwaYA5bdmt3aozim2GFr2IA3ySQwVGAmw5FimQypkJCQECdy7sSXLPlQbFktX7qso9Xf/aO7Ha1i2bLV3U/383xeFZXcrUf9fJ88kj79/H6/5/czd0dERKKrLOgCREQkWAoCEZGIUxCIiEScgkBEJOIUBCIiEacgEBGJuLwFgZndZ2b9ZvbatOe+bmY7zewVM/uxmTXma/8iIjI3+bwiuB+4ZcZzjwKXuPulwG7gS3ncv4iIzEHegsDdnwSOzXjuF+6ezDx8FliZr/2LiMjclAe4738N/HC2L5rZncCdADU1NVdt3LixUHWJiITC9u3bB9w9fq7tAgkCM/sykAS+P9s27r4V2ArQ0dHhnZ2dBapORCQczGz/XLYreBCY2R3AB4GbXRMdiYgErqBBYGa3AH8JbHH30ULuW0REziyfw0cfBH4DbDCzXjP7FPC3QB3wqJm9ZGbfztf+RURkbvJ2ReDuHz/D0/fma38iInJhdGexiEjEKQhERCJOQSAiEnEKAhGRiFMQiIhEnIJARCTiFAQiIhGnIBARiTgFgYhIxCkIREQiTkEgIhJxCgIRkYhTEIiIRJyCQEQk4hQEIiIRpyAQEYk4BYGISMQpCEREIk5BICIScQoCkfPQ1T/E1x7ZyS9e7wu6FJGcydvi9SJhc3xkgn/1nWc5OjJBeZnx+J/dwKrF1UGXJTJvuiIQmaP/8fMdnDw1yX2f7KDMjLt/tTfokkRyQkEgMgfPdA3wj9t7+fTmddy0sYXf61jJQ529HD55KujSROZNQSByDmOTU/yHH7/K6iXV3HVzGwD/7ob1pNz5zq+6A65OZP4UBCLncPcTe9l3dJT//uF3UlURA2Dlomp+98oVPPjcAfoHxwKuUGR+FAQiZ5FKOT98/iA3bWzmPW1N/9/X/viGVianUmx9UlcFUtoUBCJn8eLBE/QNjvGhy5a/7Wtrmmr48OUr+P62AxwdHg+gOpHcyFsQmNl9ZtZvZq9Ne26xmT1qZnsynxfla/8iufDzVw9TGSvjpk3NZ/z6H9/Yylhyint+3VPgykRyJ59XBPcDt8x47ovAY+7eBjyWeSxSlNydf371ML/V1kR9VcUZt2ltruUD71zGd5/Zx4nRiQJXKJIbeQsCd38SODbj6duABzL/fgD4cL72LzJfrx8a5NDJMX7nkqVn3e4zN7UyMjHFfU/vK0xhIjlW6D6CFnc/DJD5fObrbZEi8MzeAQC2tMfPut3GpfX89sUtPPDMPlIpL0RpIjlVtJ3FZnanmXWaWWcikQi6HImgp7uOsj5eQ0t91Tm3vXFjMydPTfLmCd1gJqWn0EFwxMyWAWQ+98+2obtvdfcOd++Ix8/+jkwk1yaSKZ7rOcb1rU3n3hhob6kFYPeRoXyWJZIXhQ6Ch4E7Mv++A/hpgfcvMiev9J7g1OQU162fWxC0tdQBsEtBICUon8NHHwR+A2wws14z+xTwFeB9ZrYHeF/msUjReX7fcQDetXbxnLavr6pgeUMVu/sUBFJ68jYNtbt/fJYv3ZyvfYrkyvb9x1nXVMPimso5f09bSx27jwznsSqR/CjazmKRoLg7Lxw4zpWrz+9+xw1L6+hKDDOlkUNSYhQEIjPsOzrKsZEJrjrPIGhrrmUimWL/0ZE8VSaSHwoCkRneODQIwDtXNJzX921Ymu4w1sghKTUKApEZdvYNEisz2jJDQueqtbkWM9jVp34CKS0KApEZdvYNsa6phgXlsfP6vurKclYtqmZ3v64IpLQoCERm2Nk3eLqZ53y1t9RpCKmUHAWByDTD40kOHjvFpmX1F/T9G5bW0jMwwkQylePKRPJHQSAyza7Mu/kNLRd+RZBMOT0DGjkkpUNBIDJNNgg2LrvwIABNNSGlRUEgMs3OvkHqFpSzonHhBX3/ungNsTJTP4GUFAWByDQ7Dw+xYWkdZnZB37+gPMaaJdW6l0BKioJAJMPd5zViKGvD0joFgZQUBYFIxuGTYwyOJdl4gSOGstqa69h/bJSxyakcVSaSXwoCkYzTHcU5uCJwh65+3WEspUFBIJKxoy89x9B8m4ZOjxxSh7GUCAWBSMauviFWNC6kvqpiXq+zZkk1lbEyTTUhJUNBIJKx8/DQvJuFAMpjZayL12gIqZQMBYEI6cXq9yaG590slJUeOaQ+AikNCgIRYG9imGTK5z1iKKu9pY43T5xiaGwyJ68nkk8KAhFyN2IoK9thvEcjh6QEKAhESI8YqoyVsbapJievl520bo9uLJMSoCAQIX1FsL65lopYbn4lVi5ayMKKmFYrk5KgIBAhPWJoU46ahQDKMktdaqoJKQUKAom8E6MT9A2O5WzEUFZbs+YcktKgIJDI23l6DYLcjBjK2rC0lv6hcY6PTOT0dUVyTUEgkZfrEUNZ2ZFDuiqQYqcgkMjb2TfIouoKmusW5PR1TweBhpBKkVMQSOTtmOdiNLNZ1lBF3YJyTTUhRS+QIDCzz5nZ62b2mpk9aGZVQdQh4u509Q9f8GL1Z2NmtC+t0/rFUvQKHgRmtgL4LNDh7pcAMeD2QtchApAYGmd4PMm6eG1eXr+9pZY9R4Zw97y8vkguBNU0VA4sNLNyoBo4FFAdEnHdAyNAetH5fGhvqeP46CSJ4fG8vL5ILhQ8CNz9TeCvgQPAYeCku/9i5nZmdqeZdZpZZyKRKHSZEhHdiXQQ5GpqiZnemmpCHcZSvIJoGloE3AasBZYDNWb2iZnbuftWd+9w9454PF7oMiUiegaGWVBexvKGhXl5/TatViYlIIimofcCPe6ecPdJ4EfAdQHUIULPwAhrm2ooK8vtiKGsptpKFtdU6l4CKWpBBMEB4Fozq7b0eL2bgR0B1CFCd2Ikb81CkB451NasOYekuAXRR7ANeAh4AXg1U8PWQtchMjmV4sCx0bwGAby1WplGDkmxKg9ip+7+V8BfBbFvkaze46dIpjxvQ0ez2lvqGB5PcujkGCsa89MXITIfurNYIqs7kR7Jk+8rAs05JMVOQSCR1ZO9hyDvQZC+4tBUE1KsFAQSWd0DIyyqrmBRTWVe99NYXUlL/QJNNSFFS0EgkdWdGM57s1BWe0udbiqToqUgkMjqGRjJe0dxVntLHXv6h0ilNHJIio+CQCJpZDzJkcHxgl0RbGipY2wyxcHjowXZn8j5UBBIJBWqozirLdNhrKkmpBgpCCSS3pp1tDBNQ20aQipFTEEgkdSTGMEMVi+pLsj+aheUs6JxIbvVYSxFSEEgkdQ9MMzyhoVUVcQKts/0VBO6IpDioyCQSEqPGCpM/0BWe0sdexPDTE6lCrpfkXNREEjkuDs9iZGCdRRntbfUMjnl7D86UtD9ipyLgkAiJzE8ztB4smBDR7PaTy9So34CKS4KAomcnkRhRwxltTbXUmYaOSTFR0EgkZO9h6DQVwRVFTFWL6lREEjRURBI5HQPjFBZXsbyANYGaG+p1eRzUnQUBBI53YkR1i6pIZandYrPZkNLHfuPjjI2OVXwfYvMRkEgkdMzULhZR2dqa6ljKuV0JzRySIqHgkAiJZldp7jA9xBkbViqqSak+CgIJFJ6j59icsoLfg9B1polNZSXmYJAioqCQCLl9KyjAV0RVJaXsS6ukUNSXBQEEil7MwvWr2sq7D0E07W31GnkkBQVBYFESs/ACI0FWKf4bNpb6jh47BSjE8nAahCZTkEgkdIzMBLYiKGs7FQTWsNYioWCQCKlOzESaLMQaOSQFB8FgUTGyHiSvsGxwDqKs1YtWkhFzE6vkiYSNAWBRMa+o8HMMTRTeayMixZX051Q05AUh0CCwMwazewhM9tpZjvM7N1B1CHR0p0IdujodOvitbq7WIrGrEFgZj83szV52u83gUfcfSNwGbAjT/sROS17D8HqxcEHQWtzLT0DI1qtTIrC2a4I7gd+YWZfNrOKXO3QzOqBzcC9AO4+4e4ncvX6IrPpTgyzonEhCysLt07xbFrjtSRTzv6jo0GXIjJ7ELj7PwBXAPVAp5n9mZl9Pvsxj32uAxLA35nZi2Z2j5m97S2amd1pZp1m1plIJOaxO5G0Yhg6mtXanB651NWvfgIJ3rn6CCaBEWABUDfj40KVA1cCd7v7FZnX/+LMjdx9q7t3uHtHPB6fx+5E0usUdycKv2D9bNZngmCvOoylCJTP9gUzuwX4BvAwcKW75+oathfodfdtmccPcYYgEMmloNYpnk3tgnKWN1TpikCKwqxBAHwZ+D13fz2XO3T3PjM7aGYb3H0XcDPwRi73ITLT3v5g1ik+m/XNtQoCKQqzBoG7/1Ye9/vvge+bWSXQDfxRHvclQlemCSbbNl8MWptr+eHzB0mlnLIAVksTyTrbFUHeuPtLQEcQ+5Zo2ts/TE1ljOUNVUGXclprcy2jE1McOnmKlYuqgy5HIkx3FkskdPUPs765FrPieefdGtfIISkOCgKJhK7+4dN/eItFW2YWUgWBBE1BIKE3NDZJ3+DY6SGbxWJxTSWLayo1hFQCpyCQ0NubmdOnmDqKs1rjGjkkwVMQSOjtycz7X4xBoCGkUgwUBBJ6XYlhKmLG6sXFNzKntbmW46OTHB0eD7oUiTAFgYTe3v5h1jbVUB4rvh/37FXKHl0VSICK7zdDJMe6+oeLslkINPmcFAcFgYTa2OQUB46NFt3Q0azlDVXUVMYUBBIoBYGE2r6jI6Scohs6mmVmrG+u1RBSCZSCQEIt+067WJuGQENIJXgKAgm1rv5hzGB9kTYNQfpq5fDJMYbGJoMuRSJKQSChtqd/mJWLFlJVEfzylLNpPb1IjRazl2AoCCTU9hbhHEMzaeSQBE1BIKE1lXK6B0ZOT+5WrFYvrqYiZgoCCYyCQELr4LFRJpKpor8iKI+VsbapRkEggVEQSGhl/7AW69DR6Vo1hFQCpCCQ0CrG5Sln0xqvZf/REcaTU0GXIhGkIJDQ6uofJl63gIaFFUGXck7rm2tJOewbGA26FIkgBYGEVjGuSjabtyafGwq4EokiBYGEkrsX9WRzM62P12KmIaQSDAWBhNKRwXGGx5MlEwRVFTFWLapWEEggFAQSSjv6BgHYuLS47yGYrlWrlUlAFAQSSjsPp9vaNy6tD7iSuWttrqV7YISplAddikSMgkBCaWffICsaF9JQXfwjhrJa47VMJFP0HtfIISksBYGE0o7DgyXVLARv3fi254iah6SwFAQSOuPJKfYmRti4rLSCoL0lHQS7jmgIqRRWYEFgZjEze9HMfhZUDRJOXf3DTKW8pPoHAOqqKli1eCFvHB4MuhSJmCCvCO4CdgS4fwmpbEfxpmWlFQQAFy+rZ8chBYEUViBBYGYrgQ8A9wSxfwm3HYcHWVBexpol1UGXct42Laun5+gIoxPJoEuRCAnqiuBvgL8AUrNtYGZ3mlmnmXUmEonCVSYlb2ffEO0tdZTHSq8LbNOyetzTxyBSKAX/TTGzDwL97r79bNu5+1Z373D3jng8XqDqpNS5O28cHmRTiXUUZ122shGAzn3HAq5EoiSIt0zXAx8ys33AD4CbzOzvA6hDQqj3+CmOjUxwaeYPaqlZ2lBFW3Mtv9qtq2ApnIIHgbt/yd1Xuvsa4Hbgl+7+iULXIeH0cu8JAC5fVZpBALClPc7zPcfVTyAFU3qNqCJn8fLBE1SWl7GhxG4mm25ze5yJqRTbutU8JIURaBC4+xPu/sEga5BwefngSd6xvJ6KEuwoznrX2sUsKC9T85AUTOn+tojMkJxK8eqbJ093uJaqqooY16xbwpN7FARSGAoCCY2uxDCnJqe4bFVD0KXM2+a2JroTIxw8pgnoJP8UBBIarxw8CVDyVwQAN2xID5nWVYEUgoJAQuOl3hPUVZWzZklN0KXM2/p4LcsbqnhS/QRSAAoCCY2XD57gspWNlJVZ0KXMm5mxuT3OM11HmZya9QZ8kZxQEEgojE1OsatvKBT9A1mb2+MMjSd56eCJoEuRkFMQSCi8fmiQZMpL9o7iM7l+fRNlhpqHJO8UBBIKr4TgjuKZGqoruOKiRQoCyTsFgYTCywdPsLS+ipb6qqBLyanNbXFeefMkx0Ymgi5FQkxBIKHwcu9JLl0Znv6BrM3tTbjDUxpGKnmkIJCSd2J0gp6BES4LUbNQ1qUrG2msruDJ3QNBlyIhpiCQkvdcT3pytqvXLA64ktyLlRnXtzbx1J4E7h50ORJSCgIpedt6jrGgvCxUQ0en29Iep39oXKuWSd4oCKTkbes5yhUXNbKgPBZ0KXmxuS093YRmI5V8URBISRscm+SNQ4Ncs3ZJ0KXkzdKGKja01GkYqeSNgkBK2rbuY6QcrlkXvv6B6Ta3N9G5T6uWSX4oCKSkPb6rn5rKGB2rwx4E6VXLnu0+GnQpEkIKAilZ7s4TO/t5T1sTleXh/lG+es1iqirKNIxU8iLcvz0SaruPDHPo5Bg3bmgOupS8q6qIce26JeonkLxQEEjJenxXPwA3RCAIID16qHtAq5ZJ7ikIpGQ9vrOfTcvqWdoQrvmFZrO5XcNIJT8UBFKSBscm6dx/nBszSzpGwfp4DSsaF6p5SHJOQSAl6dd7BphKOTdujEazEExbtWyvVi2T3FIQSEl6fGc/9VXlXBHCiebOZkt7E8PjSV48oFXLJHcUBFJyUinnid0JNrfHKY9F60f4utYmYmWm5iHJqWj9FkkovHDgOImhcW7eFJ1moaz6qgquWNWoDmPJKQWBlJyfvnSIqooy3nfx0qBLCcTm9jivHTrJ0eHxoEuRkCh4EJjZKjN73Mx2mNnrZnZXoWuQ0jU5leL/vnqY925qoXZBedDlBGJLexx3+HWX7jKW3AjiiiAJfMHdNwHXAn9iZhcHUIeUoKf2JDg2MsGHLlsedCmBuWRFA4uqK9Q8JDlT8CBw98Pu/kLm30PADmBFoeuQ0vSD5w6ypKYyMncTn0mszHhPW5wndw+QSmnVMpm/QPsIzGwNcAWwLcg6pDT0D47x2M5+PnrVytBPMncum9uaGBgeZ0ffYNClSAgE9ttkZrXAPwF/6u5v+2k2szvNrNPMOhMJXQILfPc3+0m5c/u7Lgq6lMBlp5vQbKSSC4EEgZlVkA6B77v7j860jbtvdfcOd++Ix6MzjYCc2clTkzzwzD5uvWQpa5tqgi4ncC31VWxcqlXLJDeCGDVkwL3ADnf/RqH3L6Xp/qf3MTSe5DM3tgVdStHY0h6nc/8xRsa1apnMTxBXBNcDfwDcZGYvZT7eH0AdUiKGxia57+ke3ruphYuX1wddTtHYsiHO5JTz8MuHgi5FSlzBB2K7+68BK/R+pXR979n9nDw1yWdvbg26lKLy7nVLuHrNIr7+L7t4/yXLaKiuCLokKVHRHnohRW90Ism9T/WwpT3OpSujNcHcuZgZ//lD7+DE6ATfeHRX0OVICVMQSFH72192cXRkgrveq76BM3nH8gY+ce1qvvfsfnYc1lBSuTAKAilaP36xl2//ai8fuXIlV160KOhyitbn39dOY3Ulf/XT13HXDWZy/hQEUpR+/GIvX/iHl7lm7RL+24cvCbqcotZYXcmf/84Gntt3jLt/tVd3G8t5UxBI0ZkeAvd98moWVsaCLqnofaxjFTdvbOZrj+zi9v/zLN2J4aBLkhKiIJCikg2Ba9cpBM5HrMy4544OvvaRS9lxeJBbvvkUdz+xl6SWtJQ5UBBI0fjxi718PhMC996hEDhfZsbHrl7FY5/fwk0bmvnqIzu57VtP89qbJ4MuTYqcgkCKQjYErluvEJiv5voqvv0HV3H371/JkcFxbvvW03z1kZ2MTU4FXZoUKQWBBG56CNzzhwqBXLn1nct47PNb+MiVK7j7ib28/5tP8VzPsaDLkiKkIJBAuDvb9x/n3zzwPJ/7oUIgXxqqK/jaRy/j7z91DZOpFB/7zm/4jz95laGxyaBLkyJipTDuuKOjwzs7O4MuQ+bp1MQULx48zrN7j/LPr/Wxp3+YRdUVfPK6tfzbLeuoqlAI5NPoRJJv/GI39z3dQ0t9Ff/1tku4eVMz6XkgJYzMbLu7d5xzuzAHwehEkomkRk2czczT76Tfrfu0rztO5r/Tj1MOqZQzOZUimXLGJ1OcPDV5xo8jg2N09Q9z8Pgo7lBmcNXqRdx2+Qp+98oVVFdGc+3hoLx08AR/+dAr7DoyRF1VOauXVLN6cQ0XLalmSU0l9VUV1FWVU1tVTl1VBQvKy4iVGWVmxMqMmBlm6ZFK2efLZmTJ9HCZGTPKnfNTXVl+wQsxKQiA//ST1/jes/vzUJHMRUXMaFhYQVPtAtY319Iar+XyVY1ctWYR9VWaIC1IE8kUP3qhlzcOD7L/6CgHjo3Se3yUyani/3sQNff/0dUXvDTrXIMg1G/Fbn3nUtbFtYjJubz9HVv6HZ+99QSW/oSR/lqZQZkZFbEyymPGgvIY9VXlNFRX0LAw/bGwIqZmhyJVWV72tpXeplLO8HiSobHJzOckw2NJxpNTJFPOVMpJuTOVgpQ7qZQzlfk8PT6mv7ec+UZTMXP+Wptr876PUAfBdeubuG59U9BliJSEWJmdDnGJFo0aEhGJOAWBiEjEKQhERCJOQSAiEnEKAhGRiFMQiIhEnIJARCTiFAQiIhGnIBARiTgFgYhIxCkIREQiTkEgIhJxCgIRkYgLJAjM7BYz22VmXWb2xSBqEBGRtIIHgZnFgG8BtwIXAx83s4sLXYeIiKQFcUXwLqDL3bvdfQL4AXBbAHWIiAjBLEyzAjg47XEvcM3MjczsTuDOzMNhM9t1jtdtAgZyUmHpieqx67ijRcd9/lbPZaMgguBMaxe+bQU7d98KbJ3zi5p1zmVtzjCK6rHruKNFx50/QTQN9QKrpj1eCRwKoA4RESGYIHgeaDOztWZWCdwOPBxAHSIiQgBNQ+6eNLPPAP8CxID73P31HLz0nJuRQiiqx67jjhYdd56Y+9ua50VEJEJ0Z7GISMQpCEREIi4UQRCVKSvMbJWZPW5mO8zsdTO7K/P8YjN71Mz2ZD4vCrrWfDCzmJm9aGY/yzxea2bbMsf9w8zgg1Axs0Yze8jMdmbO+7ujcL7N7HOZn/HXzOxBM6sK6/k2s/vMrN/MXpv23BnPsaX9r8zfulfM7Mpc1FDyQRCxKSuSwBfcfRNwLfAnmWP9IvCYu7cBj2Ueh9FdwI5pj78K/M/McR8HPhVIVfn1TeARd98IXEb6+EN9vs1sBfBZoMPdLyE9qOR2wnu+7wdumfHcbOf4VqAt83EncHcuCij5ICBCU1a4+2F3fyHz7yHSfxRWkD7eBzKbPQB8OJgK88fMVgIfAO7JPDbgJuChzCahO24zqwc2A/cCuPuEu58gAueb9IjGhWZWDlQDhwnp+Xb3J4FjM56e7RzfBnzX054FGs1s2XxrCEMQnGnKihUB1VIwZrYGuALYBrS4+2FIhwXQHFxlefM3wF8AqczjJcAJd09mHofxvK8DEsDfZZrE7jGzGkJ+vt39TeCvgQOkA+AksJ3wn+/pZjvHefl7F4YgmNOUFWFiZrXAPwF/6u6DQdeTb2b2QaDf3bdPf/oMm4btvJcDVwJ3u/sVwAghawY6k0x7+G3AWmA5UEO6SWSmsJ3vucjLz30YgiBSU1aYWQXpEPi+u/8o8/SR7OVh5nN/UPXlyfXAh8xsH+mmv5tIXyE0ZpoOIJznvRfodfdtmccPkQ6GsJ/v9wI97p5w90ngR8B1hP98TzfbOc7L37swBEFkpqzItIvfC+xw929M+9LDwB2Zf98B/LTQteWTu3/J3Ve6+xrS5/eX7v77wOPARzObhfG4+4CDZrYh89TNwBuE/HyTbhK61syqMz/z2eMO9fmeYbZz/DDwh5nRQ9cCJ7NNSPPi7iX/Abwf2A3sBb4cdD15PM73kL4MfAV4KfPxftLt5Y8BezKfFwddax7/H9wA/Czz73XAc0AX8I/AgqDry8PxXg50Zs75T4BFUTjfwH8BdgKvAd8DFoT1fAMPku4LmST9jv9Ts51j0k1D38r8rXuV9MiqedegKSZERCIuDE1DIiIyDwoCEZGIUxCIiEScgkBEJOIUBCIiEacgEDlPmVlge8xscebxoszj1UHXJnIhFAQi58ndD5Ke9fErmae+Amx19/3BVSVy4XQfgcgFyEz1sR24D/g0cIWnZ78VKTkFX7xeJAzcfdLM/hx4BPhthYCUMjUNiVy4W0lPDXBJ0IWIzIeCQOQCmNnlwPtIrxT3uVwsDiISFAWByHnKzIh5N+n1IA4AXye9kIpISVIQiJy/TwMH3P3RzOP/DWw0sy0B1iRywTRqSEQk4nRFICIScQoCEZGIUxCIiEScgkBEJOIUBCIiEacgEBGJOAWBiEjE/T8I+LveeXNJ7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = y + 1\n",
    "\n",
    "plt.plot(x,y2)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylim(0,12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the \"intensity\" method will not work well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_norm_area = rp.normalise(y2,method=\"intensity\")\n",
    "\n",
    "plt.plot(x,y_norm_area)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The signal remains shifted from 0. For safety, we can do a min-max normalisation, which will put the minimum to 0 and maximum to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_norm_area = rp.normalise(y2,method=\"minmax\")\n",
    "\n",
    "plt.plot(x,y_norm_area)\n",
    "\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
